Github user xuanyuanking commented on the issue:
https://github.com/apache/spark/pull/17702
Thanks for your review. @gatorsmile @cloud-fan
`Can you show us the performance difference?`
No problem, I reproduce our online case offline like below
## Without parallel resolve:
## With parallel resolve:
## Test env:
item | detail
--------------|---------------
Spark version | current master 2.2.0-SNAPSHOT
Hadoop version | 2.7.2
HDFS | 8 servers(128G, 20 core + 20 hyper threading)
Test case | ```spark.read.text("/app/dc/test_for_ls/*/*/*/*").count()```
<br/>first level below `test_for_ls` contains 96 directory, each directory has
1000 directory in next level, the third and forth only have 1 directory and
file each.
## Discussion:
1. More complex scenario and deeper directory level will have more
optimization, in this local test it can bring us 100% faster.
2. `spark.sql.globPathInParallel` config will only parallel the process of
resolve glob path.
3. If driver and cluster in different geographical region, this improvement
can produce at least 5* boosting in our scenario because of the resolving work
becoming a parallel job on the cluster.
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